Implementación de un control predictivo basado en modelo aplicado a un sistema de control de caudal de agua didáctico

El controlador proporcional integral derivativo -PID- se ha convertido en la herramienta de regulación de variables de proceso más utilizada, y por ello se ha aplicado de forma indiscriminada sobre sistemas dinámicos lineales y no lineales, generando problemas de control en el sector industrial, produciéndose pérdida de eficiencia en la productividad y disminución de la calidad de los productos manufacturados. Dentro de las técnicas de control moderno que han surgido para responder en aplicaciones en las cuales el controlador PID no ha operado satisfactoriamente, está el control predictivo basado en modelo -CPM-. Este algoritmo de control se ha caracterizado por su gran capacidad de respuesta, sin embargo, su expansión en el sector industrial ha sido lenta, ya que ha sido comercializado para aplicaciones particulares. Este artículo presenta resultados experimentales de la aplicación de un CPM sobre la regulación de caudal de agua en un sistema didáctico, utilizando equipos de control e instrumentación comerciales, describiéndose en el proceso la formulación del algoritmo de control, las intervenciones técnicas requeridas para la ejecución de las pruebas experimentales necesarias para obtener el modelo matemático de la planta y aplicar finalmente la técnica de control propuesta. Los resultados obtenidos evidencian las grandes posibilidades de aplicar eficientemente esta técnica de control en la regulación de variables de sistemas dinámicos generales.The proportional-integral-derivative controller -PID- has become the most used tool of regulation of process variables, and so it has been applied indiscriminately on linear and nonlinear dynamic systems, resulting in control problems in the industry, occurring loss of efficiency in productivity and decreased quality of manufactured products. Among the modern control techniques that have emerged to respond to those applications in which the PID controller has not operated satisfactorily, there is the model based predictive control -MPC-. This control algorithm has been characterized by its great capacity to respond, however, its expansion in the industrial sector has been slow, as it has been commercialized for specific applications. This paper presents an application of MPC on the regulation of water flow in a laboratory system using commercial control equipment and instrumentation, describing the formulation process control algorithm, the technical assistance required for the execution of experimental tests necessary to obtain the mathematical model of the plant and finally implementing the proposed control technique. The results evidence the great possibilities of applying effectively this control technique in variable regulation of general dynamic systems

NON-LINEAR MODEL PREDICTIVE CONTROL STRATEGIES FOR PROCESS PLANTS USING SOFT COMPUTING APPROACHES

The developments of advanced non-linear control strategies have attracted a considerable research interests over the past decades especially in process control. Rather than an absolute reliance on mathematical models of process plants which often brings discrepancies especially owing to design errors and equipment degradation, non-linear models are however required because they provide improved prediction capabilities but they are very difficult to derive. In addition, the derivation of the global optimal solution gets more difficult especially when multivariable and non-linear systems are involved. Hence, this research investigates soft computing techniques for the implementation of a novel real time constrained non-linear model predictive controller (NMPC). The time-frequency localisation characteristics of wavelet neural network (WNN) were utilised for the non-linear models design using system identification approach from experimental data and improve upon the conventional artificial neural network (ANN) which is prone to low convergence rate and the difficulties in locating the global minimum point during training process. Salient features of particle swarm optimisation and a genetic algorithm (GA) were combined to optimise the network weights. Real time optimisation occurring at every sampling instant is achieved using a GA to deliver results both in simulations and real time implementation on coupled tank systems with further extension to a complex quadruple tank process in simulations. The results show the superiority of the novel WNN-NMPC approach in terms of the average controller energy and mean squared error over the conventional ANN-NMPC strategies and PID control strategy for both SISO and MIMO systemsPetroleum Training Development Fun

A FAMILY OF MODEL PREDICTIVE CONTROL ALGORITHMS WITH ARTIFICIAL NEURAL NETWORKS

This paper details nonlinear Model-based Predictive Control (MPC) algorithms for MIMO processes modelled by means of neural networks of a feedforward structure. Two general MPC techniques are considered: the one with Nonlinear Optimisation (MPC-NO) and the one with Nonlinear Prediction and Linearisation (MPC-NPL). In the first case a nonlinear optimisation problem is solved in real time on-line. In order to reduce the computational burden, in the second case a neural model of the process is used on-line to determine local linearisation and a nonlinear free trajectory. Single-point and multi-point linearisation methods are discussed. The MPC-NPL structure is far more reliable and less computationally demanding in comparison with the MPC-NO one because it solves a quadratic programming problem, which can be done efficiently within a foreseeable time frame. At the same time, closed-loop performance of both algorithm classes is similar. Finally, a hybrid MPC algorithm with Nonlinear Prediction, Linearisation and Nonlinear optimisation (MPC-NPL-NO) is discussed